American Express is a giant multinational company with roughly 80,000 employees, so as you can imagine, something’s always coming up with IT — whether it be a worker struggling with WiFi access or dealing with a laptop on the fritz.
But as anyone knows firsthand, interacting with IT—particularly chatbots—can be a frustrating experience. Automated tools can offer vague, non-specific responses or walls of links that employees have to click through until they get to the one that actually solves their problem—that is, if they don’t give up out of frustration and click “get me to a human” first.
To upend this worn-out scenario, Amex has infused generative AI into its internal IT support chatbot. The chatbot now interacts more intuitively, adapts to feedback and walks users through problems step-by-step.
As a result, Amex has significantly decreased the number of employee IT tickets that need to be escalated to a live engineer. AI is increasingly able to resolve problems on its own.
“It’s giving people the answers, as opposed to a list of links,” Hilary Packer, Amex EVP and CTO, told VentureBeat. “Productivity is improving because we’re getting back to work quickly.”
Validation and accuracy the ‘holy grail’
The IT chatbot is just one of Amex’s many AI successes. The company has no shortage of opportunities: In fact, a dedicated council initially identified 500 potential use cases across the business, whittling that down to 70 now in various stages of implementation.
“From the beginning, we’ve wanted to make it easy for our teams to build gen AI solutions and to be compliant,” Packer explained.
That is delivered through a core enablement layer, which provides “common recipes” or starter code that engineers can follow to ensure consistency across apps. Orchestration layers connect users with models and allow them to swap models in and out based on use case. An “AI firewall” envelops all of this.
While she didn’t get into specifics, Packer explained that Amex uses open and closed-source models and tests accuracy through an extensive model risk management and validation process, including retrieval-augmented generation (RAG) and other prompt engineering techniques. Accuracy is critical in a regulated industry, and underlying data must be up to date, so her team spends a lot of time maintaining the company’s knowledge bases, validating and reformatting thousands of documents to source the best possible data.
“Validation and accuracy are the holy grail right now of generative AI,” said Packer.
AI reducing escalation by 40%
The internal IT chatbot — Amex’s most heavily used technology support function — was a natural early use case.
Initially powered by traditional natural language processing (NLP) models — specifically the open-source machine learning bidirectional encoder representations from transformers (BERT) framework — it now integrates closed-source gen AI to deliver more interactive and personalized assistance.
Packer explained that instead of simply offering a list of knowledge base articles, the chatbot engages users with follow-up questions, clarifies their issues and provides step-by-step solutions. It can generate a personalized and relevant response summarized in a clear and concise format. And if the worker still isn’t getting the answers they need, the AI can escalate unresolved problems to a live engineer.
For instance, when an employee has connectivity problems, the chatbot can offer several troubleshooting tips to get them back onto WiFi. As Packer explained, “It can get interactive with the colleague and say, ‘Did that solve your problem?’ And if they say no, it can continue on and give them other solutions.”
Since launching in October 2023, Amex has seen a 40% increase in its ability to resolve IT queries without needing to transfer to a live engineer. “We’re getting colleagues on their way, all very quickly,” said Packer.
85% of travel counselors report efficiency with AI
Amex has 5,000 travel counselors who help customize itineraries for the firm’s most elite Centurion (black) card and Platinum card members. These top-tier clients are some of the firm’s wealthiest, and expect a certain level of customer service and support. As such, counselors need to be as knowledgeable as possible about a given location.
“Travel counselors get stretched across a lot of different areas,” Packer noted. For instance, one customer may be asking about must-visit sites in Barcelona, while the next is enquiring about Buenos Aires’ five-star restaurants. “It’s trying to keep all that in somebody’s head, right?”
To optimize the process, Amex rolled out “travel counselor assist,” an AI agent that helps curate personalized travel recommendations. So, for instance, the tool can pull data from across the web (such as when a given venue is open, its peak visiting hours and nearby restaurants) that is paired with proprietary Amex data and customer data (such as what restaurant the card holder would most likely be interested in based on past spending habits). Packer said This helps create a holistic, accurate, timely view.
The AI companion now supports Amex’s 5,000 travel counselors across 19 markets — and more than 85% of them report that the tool saves them time and improves the quality of recommendations. “So it’s been a really, really productive tool,” said Packer.
While it seems AI could take over the process altogether, Packer emphasized the importance of keeping humans in the loop: The information retrieved by AI is paired with travel counselors and institutional knowledge to provide customized recommendations reflective of customers’ interests.
Because, even in this technology-driven era, customers want recommendations from a fellow human who can provide context and relevancy — not just a generic itinerary that’s been pulled together based on a basic search. “You want to know you’re talking to someone who’s going to think about the best vacation for you,” Packer noted.
AI-enhanced colleague assist, coding companion
Among its other dozens of use cases, Amex has applied AI to a “colleague help center” — similar to the IT chatbot — that has achieved a 96% accuracy rate; enhanced search optimization that returns results based on intent of words searched rather than literal words, leading to a 26% improvement in responses; and AI coding assistants that have increased developers’ productivity by 10%.
Amex’s 9,000 engineers now use GitHub Copilot, mainly for testing and code completions. Packer explained that there’s also a talk-to-your-code feature that allows developers to ask questions about the code. Eventually, the company would like to expand it across the end-to-end software development life cycle (SDLC) and to API documentation.
Notably, Packer said that more than 85% of coders have expressed satisfaction with the tool, which reflects the company’s approach to gen AI.
“Not only is it working, but when a colleague is interacting with it, do they like it?,” said Packer. “We’ve had some pilots where we’ve said we can achieve the outcome that we want, but we’re not getting great colleague satisfaction. Do we want to continue that? Is that really the right outcome for us?”
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